GPU computing group

The group aims at bringing together researchers, developers, and students interested in massivley
parallel, stream processing, thereby maintaining a local ecosystem for knowledge exchange on
parallelizability and algorithms for mathematical problems, which languages and libraries to
use, setting up the hardware, etc.

About

The group currently numbers around 15 staff and students from the schools/departments of Mathematics &
Statistics, Electrical engineering, Computer science, and NZi3. However industry GPU practitioners
and enthusiasts from greater Canterbury are very welcome. Activities include monthly meetings
and online GPU programming tutorials.

GPU get-together at the University of Canterbury, monthly

Upcoming:
Date: mid-April
Place: 446 Erskine

Previous:
Date: 2012 March 15th
Place: 446 Erskine
Speaker: discussion coordinated by Igor Rychkov
Abstract: Following the presentation of the BlueFern team at the last meeting a month ago, we should try to summarize differences in parallel computing on CPU supercomputers and on GPU cards and guidelines on choosing the right platform(s) for a specific problem. We will then look at the most recent Nvidia slides on the successful adoption of Tesla line of GPUs for HPC computers.
URL: MATH|| SageGPU: advocacy

Date: 2012 February 10th
Place: 446 Erskine
Speaker: Tony Dale and the BlueFern team
Abstract: GPUs are not the only platform available for stream processing and the SIMD model. Massive parallelization on hundreds of cores using OpenMP, OpenCL or other libraries and compilers is possible on CPU-type supercomputers.
URL: BlueFern

Date: December 15th, 11am
Place: 446 Erskine, University of Canterbury
Speakers: Tristan Read on Maximum Subarray Problem using CUDA; Dave van Leeuwen on CUDA practices and GPU cluster in the dept. of Electrical and Computer Engineering

Date: Nov 10th, 11am
Place: 446 Erskine, University of Canterbury
Speaker: "open discussion"
Abstract: "Come and join our first informal meeting on GPU computing. If you are a practitioner, please share your experience, tell us about your hardware, programming languages and libraries, difficulties and know-hows. If you are a researcher, tell us about your applications you think may benefit from massively parallel processing. We will also discuss some organisational questions of building a local eco-system for GPU developers"